'We could have asked ChatGPT': students fight back over course taught by AI Students at the University of Staffordshire have said they feel "robbed of knowledge and enjoyment" after a course they hoped would launch their digital careers turned out to be taught in large part by AI. James and Owen were among 41 students who took a coding module at Staffordshire last year, hoping to change careers through a government-funded apprenticeship programme designed to help them become cybersecurity experts or software engineers. But after a term of AI-generated slides being read, at times, by an AI voiceover, James said he had lost faith in the programme and the people running it, worrying he had "used up two years" of his life on a course that had been done "in the cheapest way possible". "If we handed in stuff that was AI-generated, we would be kicked out of the uni, but we're being taught by an AI," said James during a confrontation with his lecturer recorded as a part of the course in October 2024. James and other students confronted university officials multiple times about the AI materials. But the university appears to still be using AI-generated materials to teach the course.

Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes. Computer Science trajectories are particularly impacted, and while consistently top ranked U.S. Computer Science departments teach the mechanisms and frameworks underlying AI, few appear to offer courses on applications for existing generative AI tools. A course was developed at a private research university to teach undergraduate and graduate Computer Science students applications for generative AI tools in software development. Two mixed method surveys indicated students overwhelmingly found the course valuable and effective. Co-authored by the instructor and one of the graduate students, this paper explores the context, implementation, and impact of the course through data analysis and reflections from both perspectives. It additionally offers recommendations for replication in and beyond Computer Science departments. This is the extended version of this paper to include technical appendices.

Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such as being error-prone to manually created keywords or due to imprecise speech recognition. This paper presents a method for generating educational video recommendation using deep face-features of lecturers without identifying them. More precisely, we use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. Then, for a selected educational video taken as a reference, we recommend the ones where the presence of the same lecturers is detected. Moreover, we rank these recommended videos based on the amount of time the referenced lecturers were present. For this task, we achieved a mAP value of 99.165%.

Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a "Blind test" survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.

The influence of Artificial Intelligence (AI), and specifically Large Language Models (LLM), on education is continuously increasing. These models are frequently used by students, giving rise to the question whether current forms of assessment are still a valid way to evaluate student performance and comprehension. The theoretical framework developed in this paper is grounded in Constructive Alignment (CA) theory and Bloom's taxonomy for defining learning objectives. We argue that AI influences learning objectives of different Bloom levels in a different way, and assessment has to be adopted accordingly. Furthermore, in line with Bloom's vision, formative and summative assessment should be aligned on whether the use of AI is permitted or not. Although lecturers tend to agree that education and assessment need to be adapted to the presence of AI, a strong bias exists on the extent to which lecturers want to allow for AI in assessment. This bias is caused by a lecturer's familiarity with AI and specifically whether they use it themselves. To avoid this bias, we propose structured guidelines on a university or faculty level, to foster alignment among the staff. Besides that, we argue that teaching staff should be trained on the capabilities and limitations of AI tools. In this way, they are better able to adapt their assessment methods.

Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a secure database, and by using a custom dataset, captured some performance metrics which included student evaluation scores, Research Publications, Years of Experience, and Administrative Duties. Multilayer Perceptron (MLP) algorithm was utilized due to its ability to process complex data patterns and generates accurate predictions in a lecturer's performance based on historical data. This research focused on designing multiple performance metrics beyond the standard ones, incorporating student participation, and integrating analytical tools to deliver a comprehensive and holistic evaluation of lecturers' performance and was developed using Object-Oriented Analysis and Design (OOAD) methodology. Lecturers' performance is evaluated by the model, and the evaluation accuracy is about 91% compared with actual performance. Finally, by evaluating the performance of the MLP model, it is concluded that MLP enhanced lecturer performance evaluation by providing accurate predictions, reducing bias, and supporting data-driven decisions, ultimately improving the fairness and efficiency of the evaluation process. The MLP model's performance was evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE), achieved a test loss (MSE) of 256.99 and a MAE of 13.76, and reflected a high level of prediction accuracy. The model also demonstrated an estimated accuracy rate of approximately 96%, validated its effectiveness in predicting lecturer performance.

While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that utilizes large language models (LLMs) for knowledge graph (KG) completion, with the goal of creating personalized learning-path recommendations. Our research focuses on modelling university subjects and linking their topics to corresponding domain models, enabling the integration of learning modules from different faculties and institutions in the student's learning path. Central to our approach is a collaborative process, where LLMs assist human experts in extracting high-quality, fine-grained topics from lecture materials. We develop a domain, curriculum, and user models for university modules and stakeholders. We implement this model to create the KG from two study modules: Embedded Systems and Development of Embedded Systems Using FPGA. The resulting KG structures the curriculum and links it to the domain models. We evaluate our approach through qualitative expert feedback and quantitative graph quality metrics. Domain experts validated the relevance and accuracy of the model, while the graph quality metrics measured the structural properties of our KG. Our results show that the LLM-assisted graph completion approach enhances the ability to connect related courses across disciplines to personalize the learning experience. Expert feedback also showed high acceptance of the proposed collaborative approach for concept extraction and classification.

The email arrived out of the blue: it was the university code of conduct team. Albert, a 19-year-old undergraduate English student, scanned the content, stunned. He had been accused of using artificial intelligence to complete a piece of assessed work. If he did not attend a hearing to address the claims made by his professor, or respond to the email, he would receive an automatic fail on the module. The problem was, he hadn't cheated. The Guardian's journalism is independent.

This paper introduces SELAR, a framework designed to effectively help teachers integrate artificial intelligence (AI) into their curriculum. The framework was designed by running workshops organized to gather lecturers' feedback. In this paper, we assess the effectiveness of the framework through additional workshops organized with lecturers from the Hague University of Applied Sciences. The workshops tested the application of the framework to adapt existing courses to leverage generative AI technology. Each participant was tasked to apply SELAR to one of their learning goals in order to evaluate AI integration potential and, if successful, to update the teaching methods accordingly. Findings show that teachers were able to effectively use the SELAR to integrate generative AI into their courses. Future work will focus on providing additional guidance and examples to use the framework more effectively.

In this work we consider a new interpretation of fairness in decision making problems. Building upon existing fairness formulations, we focus on how to reason over fairness from a temporal perspective, taking into account the fairness of a history of past decisions. After introducing the concept of temporal fairness, we propose three approaches that incorporate temporal fairness in decision making problems formulated as optimization problems. We present a qualitative evaluation of our approach in four different domains and compare the solutions against a baseline approach that does not consider the temporal aspect of fairness.